Successive nuclear material balances form a time series of often autocorrelated observations. Significiant deviations from the underlying in-control process model or time series pattern, i.e., outliers, indicate an adverse process change or out-of-control situation relative to the model. This paper uses these ideas to demonstrate a method for detecting nuclear material losses earlier than currently used models and thus helps to alleviate a problem which is growing to world crisis proportions. The process control capabilities of these methods were successfully tested on two autocorrelated data sets of nuclear material balances with known removals. These algorithms should be of assistance to the nuclear engineer involved in process control.
A. INTRODUCTIONIn Nuclear Materials Accounting the goal is to accurately discover material losses (e.g., leakage or theft) as early as possible to minimize the threat to life and property. Despite some progress, control against leakage, theft, or loss of radioactive material has continued to be a problem. International terrorist activity and the desire of renegade nations to become nuclear powers make the threat of theft very real. Only recently it was discovered that controls for weapon grade nuclear materials are grossly deficient in the former Soviet Union as evidenced by several instances of stolen plutonium and uranium being found in Germany.1 Possible scenarios regarding the theft of weapons grade material are frightening.
It has previously been shown that smoothing algorithms can provide the basis for a method to detect nuclear material diversions and losses and moreover can also provide a general approach to industrial statistical process control. The present paper extends this result by showing that a set of robust smoothers also produces equivalent methods that can be used in nuclear material safeguards algorithms. Further, it is shown that these smoothers are somewhat more sensitive to loss points than the previously studied smoothers. The method is illustrated on real data.
It has previously been shown that smoothing algorithms
can provide the basis for methods to detect nuclear
material losses and moreover can also provide a general approach to
industrial statistical process control.
The present paper extends this result by showing that a set of
robust smoothers also produces methods that
can be used in statistical process control. Further, it is shown
that these smoothers are somewhat more
sensitive to out of control points than those methods previously
studied. The methods are successfully
illustrated on chemical process data.
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